Brain Cancer

Assessing large language models in radiation risk communication: susceptibility, cultural-linguistic effects, and ethical reasoning.

PURPOSE: To assess the extent to which large language models (LLMs) amplify or attenuate inaccurate or contested narratives in radiation contexts and to evaluate their potential influence on public risk perception, patient communication in radiotherapy, and radiation protection policy implementation. MATERIALS AND METHODS: We developed a structured framework to extract agreement and sentiment from...

A methodological assessment of information complementarity between MRI and GAN-based synthetic PET for multimodal medical image analysis.

Multimodal medical imaging aims to enhance analysis by combining complementary anatomical and functional information. However, access to functional modalities such as positron emission tomography (PET) is limited in many clinical settings, prompting efforts to synthesize PET-like images from magnetic resonance imaging (MRI) using generative models. Despite growing interest, the degree to which MRI...

AI-driven target definition using CE-T1w and black blood sequence imaging in stereotactic radiosurgery for brain metastases.

OBJECTIVES: Development and evaluation of a deep learning-based method for automatic detection and target delineation of brain metastases on contrast-...

A macrophage co-expression signature enables robust prognostic prediction in glioblastoma.

BACKGROUND: Glioblastoma (GBM) exhibits profound cellular heterogeneity and a highly immunosuppressive microenvironment in which tumor-associated macr...

Evaluating the Utility and Limitations of Machine Learning Tumor Segmentation for Automated Longitudinal RANO Treatment Response Classification.

PURPOSE: Machine learning segmentation has emerged in tumor assessment with high performance in volumetric evaluation of brain tumors. It is unclear, ...

Enhanced ResU-Net for brain tumor segmentation using EfficientNetB0, Channel Attention, and ASPP.

Accurate and efficient segmentation of brain tumors is critical for diagnosis, treatment planning, and monitoring in clinical practice. In this study,...

Prognostic determinants and predictive modeling in higher-grade glioma patients receiving radiotherapy: a retrospective SEER-based study.

3D vessel reconstruction from sparse-view dynamic DSA images via vessel probability guided attenuation learning.

Digital Subtraction Angiography (DSA) is one of the gold standards for vascular disease diagnosis. With the help of a contrast agent, time-resolved 2D...

Methodological appraisal practices in AI-Based radiomics research in paediatric brain tumour Imaging: A Meta-Research study of systematic reviews.

BACKGROUND: Artificial intelligence-based radiomics offers a potential adjunct to the current clinical management of paediatric brain tumours by enabl...

AI-driven navigated hematoma aspiration vs. conservative treatment for spontaneous intracerebral hemorrhage: study protocol for a randomized controlled trial.

BACKGROUND: Spontaneous intracerebral hemorrhage (ICH) remains one of the most devastating types of stroke, with high mortality and long-term disabili...

Fast Occupational Upper-Limb Radiation Dose Prediction Using Machine Learning and Monte Carlo Simulation.

OBJECTIVE: Interventional procedures expose physicians to scattered radiation, particularly to their upper extremities, posing occupational health ris...

Enhancing mesenteric vascular imaging with dual-energy CTA: a comparison of DLIR and ASIR-V using low contrast agent and low radiation dose protocols.

BACKGROUND: Timely diagnosis of mesenteric vascular diseases, especially acute mesenteric ischemia (AMI) due to embolism in the superior mesenteric ar...

A Simulation-Free Radiation Therapy Workflow Using Synthetic Computed Tomography Generated from Diagnostic Magnetic Resonance Imaging for Personalized Hippocampal-Sparing Whole-Brain Treatment.

PURPOSE: The conventional computed tomography (CT)-based consultation to simulation process for hippocampal-sparing whole-brain radiation therapy (HS-...

The University of Texas Southwestern Glioma Dataset - MRI, Molecular Markers and Segmentations.

Gliomas are the most common type of primary brain tumors. Their management options and outcomes depend significantly on the underlying molecular-marke...

FDA-authorized oncology artificial intelligence and machine learning devices and their clinical evidence: A cross-sectional analysis.

BACKGROUND: Artificial intelligence (AI) and machine learning (ML) tools are increasingly embedded in cancer care, yet the scope of U.S. Food and Drug...

Exploring the toxicological impact of perfluorooctanoic acid and perfluorooctane sulfonate on glioblastoma through network toxicology, machine learning, and multi-dimensional bioinformatics analysis.

Exposure to perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS) has been associated with the development of various malignant tumors. H...

Multi-Beholder: Biomarker Prediction for Low-Grade Glioma with Multiple Instance Learning and One-Class Classification.

Biomarker detection is an indispensable part of the diagnosis and treatment of low-grade glioma (LGG). However, current LGG biomarker detection method...

Development of a metabolic subtype classifier for low-grade glioma to guide precision therapy.

Online adaptive radiotherapy: Clinical utility, challenges and perspectives.

Online adaptive radiotherapy (oART) represents a major evolution in radiation oncology, enabling daily plan adaptation to account for anatomical varia...

AI-based synthetic CT attenuation correction enables reliable quantitative SPECT in unilateral condylar hyperplasia.

PURPOSE: Unilateral condylar hyperplasia (UCH) is a rare mandibular growth disorder in which accurate assessment of condylar metabolic activity is ess...